The Impact of Design, Position and Combination of Aerodynamic Devices on Drag and Fuel Consumption
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The performance of several aerodynamic technologies and approaches, such as trailer skirts, trailer boat tails, gap reduction, was evaluated using track testing, model wind tunnel testing, and CFD simulation, in order to assess the influence of the design, position and combination of various aerodynamic devices.</div><div class="htmlview paragraph">The track test procedure followed the SAE J1321 SAE Fuel Consumption Test Procedure - Type II. Scale model wind tunnel tests were conducted to have direct performance comparisons among several possible configurations. The wind tunnel tests were conducted on a 1/8 scale model of a tractor in combination with a 53-foot semi-trailer.</div><div class="htmlview paragraph">Among others, the wind tunnel tests and CFD simulations confirmed the influences of trailer skirts' length observed during the track tests and that the wider skirt closer to the ground offer better results.</div><div class="htmlview paragraph">The differences in the shape, dimensions and position of rear deflectors and trailer skirts on the trailer are reflected in the test results. Track testing and CFD simulation confirmed that the approach to reduce the tractor-trailer gap can result in improved fuel efficiency.</div><div class="htmlview paragraph">The tests showed that combinations of aerodynamic devices can bring additional benefits. The results obtained by combining trailer skirts and trailer rear deflectors are better than those obtained by the trailer skirts or gap deflector alone. This can be explained by the additional reduction in aerodynamic drag at the rear of the trailer, or in the gap between the tractor and trailer.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".